Prediction allows knowledge and experience to guide action and is critical for a range of sensory, motor, and cognitive functions. Failure to generate accurate predictions could contribute to neurological disorders such as autism and schizophrenia. This proposal takes advantage of an advantageous model system--a weakly electric fish--that will allow us to dissect the cellular and circuit mechanisms for predicting sensory events. Electric fish possess special receptors on their skin that allow them to detect weak electrical fields emitted by other animals in the water. This electrosense allows them to avoid predators and find prey in darkness. However, these fish also generate electrical fields of their own. Hence, a challenge for the electrosensory system is to distinguish between behaviorally relevant patterns of electrosensory input due to external events from those that are self-generated. Though particularly clear and accessible to study in electrosensory systems, this same problem faces all sensory systems. For over a century scientists and philosophers have puzzled over how we perceive a stable visual world despite the fact that visual input changes dramatically several times per second due to rapid movements of the eyes. One possible answer is that the brain generates predictions about changes in visual input that will result from our own movements and subtracts these predictions from the actual sensory input. Previous studies have shown that just such a process occurs in a region of the brain of electric fish that closely resembles the cerebellum. Previous studies have been able to directly demonstrate that predictions are formed via changes in the strength of connections between neurons, a process known as synaptic plasticity. Similar synaptic plasticity mechanisms exist in the mammalian cerebral cortex and cerebellum and are believed to underlie learning and memory. This proposal uses neural recordings and computational modeling to test the hypothesis that cerebellar granule cells generate representations of elapsed time that are critical for generating accurate predictions about temporal patterns of incoming electrosensory input. Though seminal theories proposed similar functions for granule cells in the context of cerebellar-dependent motor learning in mammals over 40 years ago, direct experimental support is still lacking. The proposed studies will provide novel insights into functions of cerebellar circuitry, neural representations of temporal information, and the neural mechanisms for predicting sensory events.